This file is mainly focus on the prelimianry selected sites by Beni that do not have the early GPP estimation
step1: tidy the table for GPP simulation vs GPP obs sites
step2: adopt the same way to separate out the model early simulation period as for the sites with early GPP estimation
library(kableExtra)
library("readxl")
table.path<-"C:/Users/yluo/Desktop/CES/Data_for_use/"
my_data <- read_excel(paste0(table.path,"Info_Table_about_Photocold_project.xlsx"), sheet = "Sites_without_earlyGPPest")
# my_data %>%
# kbl(caption = "Summary of sites with early GPP estimation") %>%
# kable_paper(full_width = F, html_font = "Cambria") %>%
# scroll_box(width = "500px", height = "200px") #with a scroll bars
my_data %>%
kbl(caption = "Summary of sites with early GPP estimation") %>%
kable_classic(full_width = F, html_font = "Cambria")
| SiteName | Delay_status | Long. | Lat. | Period | PFT | Clim. | N | Calib. | Avai.analyzed.years-spring | Avai.site-years-spring | Avai.analyzed.years-springawinter | Avai.site-years-springawinter | Reference |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| IT-Ren | No | 11.43 | 46.59 | 1998-2013 | ENF | Dfc | 3405 | Y | 2002-2003,2005-2013 | 11 | 2002-2003,2005-2013 | 11 | Montagnani et al. (2009) |
| RU-Ha1 | No | 90.00 | 54.73 | 2002-2004 | GRA | Dfc | 567 | NA | no early years (2002-2004 lack early doy) | 0 | no early years (2002-2004 lack early doy) | 0 | Belelli Marchesini et al. (2007) |
| BE-Vie | No | 6.00 | 50.31 | 1996-2014 | MF | Cfb | 4910 | Y | 2000-2014 | 15 | 2000-2014 | 15 | Aubinet et al. (2001) |
| CH-Cha | No | 8.41 | 47.21 | 2005-2014 | GRA | Cfb | 2944 | NA | 2006-2008,2010-2014 | 8 | 2006-2008,2010-2014 | 8 | Merbold et al. (2014) |
| CH-Lae | No | 8.37 | 47.48 | 2004-2014 | MF | Cfb | 3551 | Y | 2005-2014(2004 lack early doy) | 10 | 2005-2014(2004 lack early doy) | 10 | Etzold et al. (2011) |
| CH-Oe1 | No | 7.73 | 47.29 | 2002-2008 | GRA | Cfb | 2184 | Y | 2002-2008 | 7 | 2002-2008 | 7 | Ammann et al. (2009) |
| DE-Gri | No | 13.51 | 50.95 | 2004-2014 | GRA | Cfb | 3642 | Y | 2004-2014 | 11 | 2004-2014 | 11 | Prescher et al. (2010) |
| DE-Obe | No | 13.72 | 50.78 | 2008-2014 | ENF | Cfb | 2260 | Y | 2008-2014 | 7 | 2008-2014 | 7 | NA |
| DE-RuR | No | 6.30 | 50.62 | 2011-2014 | GRA | Cfb | 1227 | Y | 2012-2014 | 3 | 2012-2014 | 3 | Post et al. (2015) |
| DE-Tha | No | 13.57 | 50.96 | 1996-2014 | ENF | Cfb | 5141 | Y | 2000-2014 | 15 | 2000-2014 | 15 | Grünwald and Bernhofer (2007) |
| NL-Hor | No | 5.07 | 52.24 | 2004-2011 | GRA | Cfb | 2188 | Y | 2005,2007-2011 | 6 | 2005,2007-2010 | 5 | Jacobs et al. (2007) |
| NL-Loo | No | 5.74 | 52.17 | 1996-2013 | ENF | Cfb | 4671 | Y | 2000-2013 | 14 | 2000-2013 | 14 | Moors (2012) |
| Sum | NA | NA | NA | NA | NA | NA | 36690 | NA | NA | 107 | NA | 106 | NA |
## [1] 5
(2) For Cfb:for GRA, MF and ENF sites
## [1] 8
## [1] 7
## [1] 11
## [1] 3
## [1] 6
- Cfb-ENF (3 site)
## [1] 7
## [1] 15
## [1] 14
```
Step1: normlization for all the years in one site
#normalized the gpp_obs and gpp_mod using the gpp_max(95 percentile of gpp)
Step 2:Determine the green-up period for each year(using spline smoothed values):
#followed analysis is based on the normlized “GPP_mod”time series(determine earlier sos)
using the normalized GPP_mod to determine sos,eos and peak of the time series (using the threshold, percentile 10 of amplitude, to determine the sos and eos in this study). We selected the GPP_mod to determine the phenophases as genearlly we can get earlier sos compared to GPP_obs–> we can have larger analysis period
Step 3:rolling mean of GPPobs and GPPmod for data for all the years(moving windown:5,7,10, 15, 20days)
also for the data beyond green-up period–> the code of this steps moves to second step
Step 4:Fit the Guassian norm distribution for residuals beyond the green-up period
The reason to conduct this are: we assume in general the P-model assume the GPP well outside the green-up period (compared to the observation data).
But in practise, the model performance is not always good beyond the green-up period–>I tested three data range:
[peak,265/366]
DoY[1, sos]& DOY[peak,365/366]
[1,sos] & [eos,365/366]
I found the using the data range c, the distrbution of biase (GPP_mod - GPP_obs) is more close to the norm distribution, hence at end of I used the data range c to build the distribution.
step 5:determine the “is_event” within green-up period
After some time of consideration, I took following crition to determine the “is_event”:
during the growing season period (sos,eos)–>the data with GPP biases bigger than 3 SD are classified as the “GPP overestimation points”
For “GPP overstimation points”, thoses are air temparture is less than 10 degrees will be classified as the “is_event”. I selected 10 degree as the crition by referring to the paper Duffy et al., 2021 and many papers which demonstrate the temperature response curve normally from 10 degree (for instance: Lin et al., 2012)
References:
Duffy et al., 2021:https://advances.sciencemag.org/content/7/3/eaay1052
Lin et al., 2012:https://academic.oup.com/treephys/article/32/2/219/1657108
step 6:Evaluation “is_event”–>visualization and stats
visulization
stats: \[ Pfalse = \frac{days(real_{(is-event)})}{days(flagged_{(is-event)})} \]